File size: 8,327 Bytes
80bce48
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
from typing import Any, Dict, Optional

import torch
import torch.nn as nn
from transformers import AutoConfig, AutoModel, PreTrainedModel
from transformers.modeling_outputs import (
    BaseModelOutputWithPooling,
    MaskedLMOutput,
    BaseModelOutput,
    SequenceClassifierOutput,
)
from enum import Enum

from .config import ILKTConfig

def cls_pooling(last_hidden_state, attention_mask):
    return last_hidden_state[:, 0, :]


def create_head_blocks(
    hidden_size: int,
    n_dense: int,
    use_batch_norm: bool,
    use_layer_norm: bool,
    dropout: float,
    **kwargs,
) -> nn.Module:
    blocks = []
    for _ in range(n_dense):
        blocks.append(nn.Linear(hidden_size, hidden_size))
        if use_batch_norm:
            blocks.append(nn.BatchNorm1d(hidden_size))
        elif use_layer_norm:
            blocks.append(nn.LayerNorm(hidden_size))
        blocks.append(nn.ReLU())
        if dropout > 0:
            blocks.append(nn.Dropout(dropout))
    return nn.Sequential(*blocks)


class SentenceEmbeddingHead(nn.Module):
    def __init__(
        self, backbone_hidden_size: int, embedding_head_config: Dict[str, Any]
    ):
        super().__init__()
        self.config = embedding_head_config

        self.head = nn.Sequential(
            *[
                create_head_blocks(backbone_hidden_size, **embedding_head_config),
            ]
        )

    def forward(
        self, backbone_output: BaseModelOutput, attention_mask: torch.Tensor, **kwargs
    ) -> BaseModelOutputWithPooling:
        if self.config["pool_type"] == "cls":
            embeddings = cls_pooling(backbone_output.last_hidden_state, attention_mask)
        else:
            raise NotImplementedError(
                f"Pooling type {self.config['pool_type']} not implemented"
            )
        embeddings = self.head(embeddings)
        if self.config["normalize_embeddings"]:
            embeddings = nn.functional.normalize(embeddings, p=2, dim=-1)
        return BaseModelOutputWithPooling(
            last_hidden_state=backbone_output.last_hidden_state,
            pooler_output=embeddings,  # type: ignore
        )


class MLMHead(nn.Module):
    def __init__(
        self,
        backbone_hidden_size: int,
        vocab_size: int,
        mlm_head_config: Dict[str, Any],
    ):
        super().__init__()
        self.config = mlm_head_config

        self.head = nn.Sequential(
            *[
                create_head_blocks(backbone_hidden_size, **mlm_head_config),
                nn.Linear(backbone_hidden_size, vocab_size),
            ]
        )

    def forward(
        self,
        backbone_output: BaseModelOutput,
        attention_mask: torch.Tensor,
        labels: Optional[torch.Tensor] = None,
        **kwargs,
    ) -> MaskedLMOutput:
        prediction_scores = self.head(backbone_output.last_hidden_state)

        loss = None
        if labels is not None:
            loss_fct = nn.CrossEntropyLoss()
            loss = loss_fct(
                prediction_scores.view(-1, prediction_scores.size(-1)),
                labels.view(-1),
            )
        return MaskedLMOutput(loss=loss, logits=prediction_scores)


class CLSHead(nn.Module):
    def __init__(
        self,
        backbone_hidden_size: int,
        n_classes: int,
        cls_head_config: Dict[str, Any],
    ):
        super().__init__()
        self.config = cls_head_config

        self.head = nn.Sequential(
            *[
                create_head_blocks(backbone_hidden_size, **cls_head_config),
                nn.Linear(backbone_hidden_size, n_classes),
            ]
        )

    def forward(
        self,
        backbone_output: BaseModelOutput,
        attention_mask: torch.Tensor,
        labels: Optional[torch.Tensor] = None,
        **kwargs,
    ) -> SequenceClassifierOutput:
        if self.config["pool_type"] == "cls":
            embeddings = cls_pooling(backbone_output.last_hidden_state, attention_mask)
        else:
            raise NotImplementedError(
                f"Pooling type {self.config['pool_type']} not implemented"
            )

        prediction_scores = self.head(embeddings)

        loss = None
        if labels is not None:
            loss_fct = nn.CrossEntropyLoss()
            loss = loss_fct(
                prediction_scores.view(-1, prediction_scores.size(-1)),
                labels.view(-1),
            )
        return SequenceClassifierOutput(loss=loss, logits=prediction_scores)


class ForwardRouting(Enum):
    GET_SENTENCE_EMBEDDING = "get_sentence_embedding"
    GET_MLM_OUTPUT = "get_mlm_output"
    GET_CLS_OUTPUT = "get_cls_output"


class ILKTModel(PreTrainedModel):
    config_class = ILKTConfig

    def __init__(self, config: ILKTConfig):
        super().__init__(config)

        backbone_config = AutoConfig.from_pretrained(**config.backbone_config)
        pretrained_model_name_or_path = config.backbone_config[
            "pretrained_model_name_or_path"
        ]
        self.backbone = AutoModel.from_pretrained(
            pretrained_model_name_or_path, config=backbone_config
        )

        backbone_hidden_size = backbone_config.hidden_size
        self.config.hidden_size = backbone_hidden_size
        backbone_vocab_size = backbone_config.vocab_size
        self.embedding_head = SentenceEmbeddingHead(
            backbone_hidden_size, config.embedding_head_config
        )
        self.mlm_head = MLMHead(
            backbone_hidden_size, backbone_vocab_size, config.mlm_head_config
        )

        self.cls_heads = nn.ModuleDict(
            dict(
                [
                    (
                        name,
                        CLSHead(
                            backbone_hidden_size, n_classes, config.cls_head_config
                        ),
                    )
                    for n_classes, name in config.cls_heads
                ]
            )
        )

    def forward(
        self,
        input_ids: torch.Tensor,
        attention_mask: torch.Tensor,
        token_type_ids: Optional[torch.Tensor] = None,
        forward_routing: ForwardRouting = ForwardRouting.GET_SENTENCE_EMBEDDING,
        **kwargs,
    ):
        if forward_routing == ForwardRouting.GET_SENTENCE_EMBEDDING:
            return self.get_sentence_embedding(
                input_ids, attention_mask, token_type_ids=token_type_ids
            )
        elif forward_routing == ForwardRouting.GET_MLM_OUTPUT:
            return self.get_mlm_output(
                input_ids, attention_mask, token_type_ids=token_type_ids, **kwargs
            )
        elif forward_routing == ForwardRouting.GET_CLS_OUTPUT:
            return self.get_cls_output(
                input_ids, attention_mask, token_type_ids=token_type_ids, **kwargs
            )
        else:
            raise ValueError(f"Unknown forward routing {forward_routing}")

    def get_sentence_embedding(
        self, input_ids: torch.Tensor, attention_mask: torch.Tensor, **kwargs
    ):
        backbone_output: BaseModelOutput = self.backbone(
            input_ids=input_ids, attention_mask=attention_mask, **kwargs
        )

        embedding_output = self.embedding_head(
            backbone_output, attention_mask, **kwargs
        )

        return embedding_output

    def get_mlm_output(
        self,
        input_ids: torch.Tensor,
        attention_mask: torch.Tensor,
        labels: Optional[torch.Tensor] = None,
        **kwargs,
    ):
        backbone_output: BaseModelOutput = self.backbone(
            input_ids=input_ids, attention_mask=attention_mask, **kwargs
        )

        mlm_output = self.mlm_head(backbone_output, attention_mask, labels, **kwargs)

        return mlm_output

    def get_cls_output(
        self,
        input_ids: torch.Tensor,
        attention_mask: torch.Tensor,
        head_name: str,
        labels: Optional[torch.Tensor] = None,
        **kwargs,
    ):
        backbone_output: BaseModelOutput = self.backbone(
            input_ids=input_ids, attention_mask=attention_mask, **kwargs
        )

        if head_name not in self.cls_heads:
            raise ValueError(f"Head {head_name} not found in model")

        cls_output = self.cls_heads[head_name](
            backbone_output, attention_mask, labels, **kwargs
        )

        return cls_output